1. Technical Field
The present invention relates generally to character recognition for recognizing a cursive style of Hangul (Korean alphabet) letters and English letters by a pen computer, a handwritten character input electronic pocket book, or a handwritten character input document editor and the like, and more particularly to an online handwritten character recognizing system and method thereof for effectively recognizing strokes of complicated characters such as the cursive style of Hangul or English even when character strokes are inputted via a personal writing style, by utilizing Fourier transformation and a neural network arrangement.
2. Background Art
Generally, automatic character recognizing systems utilizing Fourier transformation recognize a handwritten character inputted through an input means, such as a tablet, with a series of X and Y coordinates produced while writing the strokes of the character. The series of X coordinates and Y coordinates are then Fourier transformed, whereby Fourier coefficients are produced. The Fourier coefficients obtained from above-described calculation is compared with Fourier coefficients of standard characters that have been previously determined and stored in a memory storage. The standard character that is the most similar to the handwritten character is then recognized.
The conventional online handwritten character recognizing system utilizing Fourier transformation typically comprises, as shown in the accompanying FIG. 1, a preprocessing section 2 for filtering out any points that may be present at a distance away from the X coordinates and Y coordinates of the main points of a character stroke input through a tablet input/output section 1. The preprocessing section 2 is also used for normalizing the change of magnitude and time difference (speed) of writing the input points to a predetermined magnitude and distance. The conventional character recognizing system also includes a feature extracting section 3 for providing Fourier coefficients of character strokes by Fourier transformation (through the preprocessing section 2) of the coordinate points having the predetermined magnitude of points and the predetermined number of points, and a character recognizing section 5 for comparing the Fourier coefficients of character strokes obtained from the feature extracting section 3 with Fourier coefficients of standard character strokes stored in a database of a standard input pattern section 4. The character recognizing section 5 is also used for recognizing a difference between the standard stroke and regions of the input strokes. Finally, the character recognizing system involves a recognition result section 6 for transmitting a result recognized by the character recognizing section 5 to the tablet input/output section 1.
According to the conventional online handwritten character recognizing system described above, when the points of the character are input through the tablet input/output section 1, the preprocessing section 2 filters out the points which are present at a predetermined distance from the other input points to normalize the magnitude of the remaining input points for recognizing the character, irrespective of any change in the magnitude of the input points.
A conventional method of comparing input data to standard character stokes uses the Bayesian Decision Rule. The Bayesian Decision Rule imposes black marks upon detecting dissimilarity between the standard strokes and the inputted strokes. The black marks are imposed in cases where a starting region of the input stroke differs from the standard strokes, in cases where more data than that corresponding to standard character strokes are present, in cases where less data relative to the standard character strokes are present, in cases where any differences with the standard strokes are present after comparison to the input strokes, and in cases where differences between regions of the strokes are present. Standard strokes which have the least black marks imposed after comparing the standard strokes with the inputted strokes are outputted as a recognized result.
Unfortunately, these handwritten character recognizing systems that use a conventional Fourier transformation have had shortcomings in that calculating time is considerably long because of the separation of X axis and Y axis coordinates when seeking the Fourier coefficients of English character strokes that are obtained from the feature extracting section. Recognition time becomes even longer due to the processing of obscurities that may appear which do not match any of the standard characters when comparing standard characters to the inputted character in the character recognizing section. Additional problems occur with persons having a particular writing style, for which use of character recognition devices is frustrating since conventional devices are not always able to recognize unconventional writing styles when using a standard character selection as a reference.